




'''simple docstring'''







import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel

class 	_snake_case:






 def __init__(self       :  Union[str, Any]    ,		a       :  Dict    ,		a       :  Union[str, Any]=99    ,		a       :  str=13    ,		a       :  List[Any]=7    ,		a       :  Dict=9    ,		a       :  Optional[int]=True    ,		a       :  List[str]=True    ,		a       :  str=False    ,		a       :  Dict=32    ,		a       :  int=5    ,		a       :  List[Any]=4    ,		a       :  List[Any]=37    ,		a       :  List[Any]=8    ,		a       :  int=0.1    ,		a       :  Union[str, Any]=0.002    ,		a       :  int=1    ,		a       :  int=0    ,		a       :  Optional[Any]=0    ,		a       :  Tuple=None    ,		a       :  Any=None    ,		)       ->   str:






    """simple docstring"""


    A__       =     parent
    A__       =     batch_size
    A__       =     encoder_seq_length
    A__       =     decoder_seq_length
    # For common tests
    A__       =     self.decoder_seq_length
    A__       =     is_training
    A__       =     use_attention_mask
    A__       =     use_labels
    A__       =     vocab_size
    A__       =     hidden_size
    A__       =     num_hidden_layers
    A__       =     num_attention_heads
    A__       =     d_ff
    A__       =     relative_attention_num_buckets
    A__       =     dropout_rate
    A__       =     initializer_factor
    A__       =     eos_token_id
    A__       =     pad_token_id
    A__       =     decoder_start_token_id
    A__       =     None
    A__       =     decoder_layers






 def 						_UpperCamelCase (self       :  Dict    )       ->   Union[str, Any]:






    """simple docstring"""


    return TaConfig.from_pretrained('google/umt5-base'    )






 def 						_UpperCamelCase (self       :  Optional[int]    ,		a       :  Dict    ,		a       :  int    ,		a       :  Any    ,		a       :  Optional[Any]=None    ,		a       :  str=None    ,		a       :  Optional[int]=None    ,		a       :  Optional[Any]=None    ,		a       :  List[str]=None    ,		)       ->   Union[str, Any]:






    """simple docstring"""


    if attention_mask is None:
       A__       =     input_ids.ne(config.pad_token_id    )
    if decoder_attention_mask is None:
       A__       =     decoder_input_ids.ne(config.pad_token_id    )
    if head_mask is None:
       A__       =     torch.ones(config.num_hidden_layers    ,		config.num_attention_heads    ,		device=a    )
    if decoder_head_mask is None:
       A__       =     torch.ones(config.num_decoder_layers    ,		config.num_attention_heads    ,		device=a    )
    if cross_attn_head_mask is None:
       A__       =     torch.ones(
           config.num_decoder_layers    ,		config.num_attention_heads    ,		device=a    )
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": decoder_attention_mask,
        "head_mask": head_mask,
        "decoder_head_mask": decoder_head_mask,
        "cross_attn_head_mask": cross_attn_head_mask,
    }






 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   int:






    """simple docstring"""


    A__       =     ids_tensor([self.batch_size, self.encoder_seq_length]    ,		self.vocab_size    )
    A__       =     ids_tensor([self.batch_size, self.decoder_seq_length]    ,		self.vocab_size    )

    # we need to clamp the input ids here to avoid having pad token in between
    # this is because for NllbMoe the position_ids are prepared such that
    # all pad tokens have pos id = 2 and rest are between 2..seq_length
    # and the seq_length here is seq_length - num_pad_tokens
    # but when using past, there is no way of knowing if the past input ids had
    # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
    # position_ids being off by num_pad_tokens in past input
    A__       =     input_ids.clamp(self.pad_token_id + 1    )
    A__       =     decoder_input_ids.clamp(self.pad_token_id + 1    )

    A__       =     self.get_config()
    A__       =     config.num_attention_heads
    A__       =     self.prepare_inputs_dict(a    ,		a    ,		a    )
    return config, input_dict






 def 						_UpperCamelCase (self       :  Tuple    )       ->   str:






    """simple docstring"""


    A__  ,			A__       =     self.prepare_config_and_inputs()
    return config, inputs_dict






 def 						_UpperCamelCase (self       :  List[Any]    )       ->   Dict:






    """simple docstring"""


    return TaConfig(
        vocab_size=1_66    ,		d_model=self.hidden_size    ,		d_ff=self.d_ff    ,		d_kv=self.hidden_size // self.num_attention_heads    ,		num_layers=self.num_hidden_layers    ,		num_decoder_layers=self.decoder_layers    ,		num_heads=self.num_attention_heads    ,		relative_attention_num_buckets=self.relative_attention_num_buckets    ,		dropout_rate=self.dropout_rate    ,		initializer_factor=self.initializer_factor    ,		eos_token_id=self.eos_token_id    ,		bos_token_id=self.pad_token_id    ,		pad_token_id=self.pad_token_id    ,		decoder_start_token_id=self.decoder_start_token_id    ,		)






 def 						_UpperCamelCase (self       :  List[Any]    )       ->   Dict:






    """simple docstring"""


    return TaConfig(
        vocab_size=self.vocab_size    ,		d_model=self.hidden_size    ,		d_ff=self.d_ff    ,		d_kv=self.hidden_size // self.num_attention_heads    ,		num_layers=self.num_hidden_layers    ,		num_decoder_layers=self.decoder_layers    ,		num_heads=self.num_attention_heads    ,		relative_attention_num_buckets=self.relative_attention_num_buckets    ,		dropout_rate=self.dropout_rate    ,		initializer_factor=self.initializer_factor    ,		eos_token_id=self.eos_token_id    ,		bos_token_id=self.pad_token_id    ,		pad_token_id=self.pad_token_id    ,		decoder_start_token_id=self.decoder_start_token_id    ,		)






 def 						_UpperCamelCase (self       :  List[str]    ,		a       :  Optional[int]    ,		a       :  str    ,		a       :  Tuple    ,		a       :  Tuple    ,		a       :  List[str]    ,		a       :  Tuple    ,		)       ->   List[str]:






    """simple docstring"""


    A__       =     UMTaModel(config=a    )
    model.to(a    )
    model.eval()
    A__       =     model(
        input_ids=a    ,		decoder_input_ids=a    ,		attention_mask=a    ,		decoder_attention_mask=a    ,		)
    A__       =     model(input_ids=a    ,		decoder_input_ids=a    )
    A__       =     result.last_hidden_state
    A__       =     result.past_key_values
    A__       =     result.encoder_last_hidden_state

    self.parent.assertEqual(encoder_output.size()    ,		(self.batch_size, self.encoder_seq_length, self.hidden_size)    )
    self.parent.assertEqual(decoder_output.size()    ,		(self.batch_size, self.decoder_seq_length, self.hidden_size)    )
    # There should be `num_layers` key value embeddings stored in decoder_past
    self.parent.assertEqual(len(a    )    ,		config.num_layers    )
    # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
    self.parent.assertEqual(len(decoder_past[0]    )    ,		4    )






 def 						_UpperCamelCase (self       :  Any    ,		a       :  List[str]    ,		a       :  Optional[Any]    ,		a       :  Optional[Any]    ,		a       :  int    ,		a       :  str    ,		a       :  Union[str, Any]    ,		)       ->   Any:






    """simple docstring"""


    A__       =     UMTaModel(config=a    ).get_decoder().to(a    ).eval()
    # first forward pass
    A__       =     model(a    ,		use_cache=a    )
    A__       =     model(a    )
    A__       =     model(a    ,		use_cache=a    )

    self.parent.assertTrue(len(a    ) == len(a    )    )
    self.parent.assertTrue(len(a    ) == len(a    ) + 1    )

    A__  ,			A__       =     outputs.to_tuple()

    # create hypothetical next token and extent to next_input_ids
    A__       =     ids_tensor((self.batch_size, 1)    ,		config.vocab_size    )

    # append to next input_ids and
    A__       =     torch.cat([input_ids, next_tokens]    ,		dim=-1    )

    A__       =     model(a    )['last_hidden_state']
    A__       =     model(a    ,		past_key_values=a    )['last_hidden_state']

    # select random slice
    A__       =     ids_tensor((1,)    ,		output_from_past.shape[-1]    ).item()
    A__       =     output_from_no_past[:, -1, random_slice_idx].detach()
    A__       =     output_from_past[:, 0, random_slice_idx].detach()

    # test that outputs are equal for slice
    self.parent.assertTrue(torch.allclose(a    ,		a    ,		atol=1e-3    )    )






 def 						_UpperCamelCase (self       :  Dict    ,		a       :  Dict    ,		a       :  List[Any]    ,		)       ->   Optional[int]:






    """simple docstring"""


    A__       =     UMTaModel(config=a    ).to(a    ).half().eval()
    A__       =     model(**a    )['last_hidden_state']
    self.parent.assertFalse(torch.isnan(a    ).any().item()    )




@require_torch
class 	_snake_case(			UpperCAmelCase       , UpperCAmelCase       , UpperCAmelCase       , unittest.TestCase      ):
 __snake_case:							Union[str, Any]						=					(
     (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
 )
 __snake_case:							Optional[Any]						=					(UMTaForConditionalGeneration,) if is_torch_available() else ()
 __snake_case:							List[str]						=					(
     {
         '''conversational''': UMTaForConditionalGeneration,
         '''feature-extraction''': UMTaModel,
         '''summarization''': UMTaForConditionalGeneration,
         '''text2text-generation''': UMTaForConditionalGeneration,
         '''translation''': UMTaForConditionalGeneration,
         '''question-answering''': UMTaForQuestionAnswering,
     }
     if is_torch_available()
     else {}
 )
 __snake_case:							Union[str, Any]						=					True
 __snake_case:							List[Any]						=					False
 __snake_case:							str						=					False
 __snake_case:							Optional[Any]						=					True
 __snake_case:							str						=					True
 # The small UMT5 model needs higher percentages for CPU/MP tests
 __snake_case:							Any						=					[0.8, 0.9]






 def 						_UpperCamelCase (self       :  Any    )       ->   Tuple:






    """simple docstring"""


    A__       =     UMTaModelTester(self    )






 @unittest.skip('Test has a segmentation fault on torch 1.8.0'    )
 def 						_UpperCamelCase (self       :  List[Any]    )       ->   Dict:






    """simple docstring"""


    A__       =     self.model_tester.prepare_config_and_inputs()
    A__       =     UMTaModel(config_and_inputs[0]    ).to(a    )
    with tempfile.TemporaryDirectory() as tmpdirname:
       torch.onnx.export(
           a    ,		(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])    ,		f"""{tmpdirname}/t5_test.onnx"""    ,		export_params=a    ,		opset_version=9    ,		input_names=['input_ids', 'decoder_input_ids']    ,		)






 @unittest.skipIf(torch_device == 'cpu'    ,		'Cant do half precision'    )
 def 						_UpperCamelCase (self       :  Any    )       ->   Optional[Any]:






    """simple docstring"""


    A__       =     self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model_fpaa_forward(*a    )






 def 						_UpperCamelCase (self       :  Optional[int]    )       ->   Optional[int]:






    """simple docstring"""


    A__       =     ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
    A__       =     self.model_tester.prepare_config_and_inputs()
    A__       =     config_and_inputs[0]
    A__       =     UMTaForConditionalGeneration(a    ).eval()
    model.to(a    )

    A__       =     {
        'head_mask': torch.zeros(config.num_layers    ,		config.num_heads    ,		device=a    ),
        'decoder_head_mask': torch.zeros(config.num_decoder_layers    ,		config.num_heads    ,		device=a    ),
        'cross_attn_head_mask': torch.zeros(config.num_decoder_layers    ,		config.num_heads    ,		device=a    ),
    }

    for attn_name, (name, mask) in zip(a    ,		head_masking.items()    ):
       A__       =     {name: mask}
       # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
       if name == "head_mask":
          A__       =     torch.ones(
              config.num_decoder_layers    ,		config.num_heads    ,		device=a    )

       A__       =     model.generate(
           config_and_inputs[1]['input_ids']    ,		num_beams=1    ,		max_length=3    ,		output_attentions=a    ,		return_dict_in_generate=a    ,		**a    ,		)
       # We check the state of decoder_attentions and cross_attentions just from the last step
       A__       =     out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
       self.assertEqual(sum([w.sum().item() for w in attn_weights]    )    ,		0.0    )






 @unittest.skip('Does not work on the tiny model as we keep hitting edge cases.'    )
 def 						_UpperCamelCase (self       :  Any    )       ->   List[str]:






    """simple docstring"""


    pass




@require_torch
@require_sentencepiece
@require_tokenizers
class 	_snake_case(			unittest.TestCase      ):






 @slow
 @unittest.skip(
     'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'    )
 def 						_UpperCamelCase (self       :  str    )       ->   List[str]:






    """simple docstring"""


    A__       =     UMTaForConditionalGeneration.from_pretrained('google/umt5-small'    ,		return_dict=a    ).to(a    )
    A__       =     AutoTokenizer.from_pretrained('google/umt5-small'    ,		use_fast=a    ,		legacy=a    )
    A__       =     [
        'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
        'No se como puedo <extra_id_0>.',
        'This is the reason why we <extra_id_0> them.',
        'The <extra_id_0> walks in <extra_id_1>, seats',
        'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
    ]
    A__       =     tokenizer(a    ,		return_tensors='pt'    ,		padding=a    ).input_ids
    # fmt: off
    A__       =     torch.tensor(
        [
            [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
            [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
            [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
            [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
            [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
        ]    )
    # fmt: on
    torch.testing.assert_allclose(a    ,		a    )

    A__       =     model.generate(input_ids.to(a    )    )
    A__       =     [
        '<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
        '<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
        '<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
        '<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
        '<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
    ]
    A__       =     tokenizer.batch_decode(a    )
    self.assertEqual(a    ,		a    )








'''simple docstring'''







from torch import nn







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   if act_fn in ["swish", "silu"]:
      return nn.SiLU()
   elif act_fn == "mish":
      return nn.Mish()
   elif act_fn == "gelu":
      return nn.GELU()
   else:
      raise ValueError(F"""Unsupported activation function: {act_fn}"""						)


